CN101847262A - Fast three-dimensional point cloud searching and matching method - Google Patents
Fast three-dimensional point cloud searching and matching method Download PDFInfo
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Abstract
The invention relates to a fast three-dimensional point cloud searching and matching method, which comprises the steps of main shaft registration, measuring shaft generation, low-precision searching, high-precision searching and primary iteration result generation. The main shaft registration link is responsible for the defining point cloud direction and completes the initial registration, the measuring shaft generation link provides the searching range in the current iteration for moving point clouds, the low-precision searching adopts an ergodic method for looking for the optimal registration posture of the moving point clouds on the current measuring shaft, the high-precision searching is responsible for obtaining better optimal registration posture at smaller step length searching position errors on the basis of the low-precision searching, and the primary iteration result generation link is responsible for generating the final results of the primary iteration according to the measuring results in each position. The invention starts from the geometrical shape of the point clouds, and completes the ergodic searching at different precisions through the mutual movement and rotation between two point clouds to find the optimal matching position, the algorithm has good fault-tolerance performance, and the invention is suitable for any three-dimensional digital instrument to obtain the searching matching of the three-dimensional point clouds.
Description
Technical field
The present invention relates to a kind of fast three-dimensional point cloud searching and matching method, belong to remote sensing and mapping, computer vision and mode identification technology, be applicable to the three dimensional point cloud that any 3D digitizer obtains.
Background technology
The three-dimensional point cloud registration is the problem that computer vision and pattern-recognition, remote sensing and survey field are extremely paid close attention to always.Existing method for registering is sought closest approach by iterative manner based on the ICP algorithm mostly between 2 clouds, thereby realizes some cloud registration.If there is analog structure in the scene, iteration tends to be absorbed in local minimum, the mistake coupling occurs, thereby causes whole registration failure.
Summary of the invention
Technology of the present invention is dealt with problems: overcome the deficiencies in the prior art, a kind of fast three-dimensional point cloud searching and matching method is provided, this method is started with from a cloud geometric configuration, finish the traversal search under the different accuracy by mobile mutually, rotation between 2 clouds, find best match position, the algorithm zmodem is fit to the search matched that any 3D digitizer obtains three-dimensional point cloud.
Technical solution of the present invention: a kind of fast three-dimensional point cloud searching method for registering is characterized in that:
(1) under the situation that 2 cloud geometric center point overlap, defining point cloud middle distance geometric center point is some cloud direction apart from the solstics, and the line of this point and geometric center point is finished the match of two subject to registration somes cloud main shafts for some cloud main shaft.
(2) being starting point with 2 cloud geometric center point, is direction with moving some cloud main shaft, generates No. 0 measuring center axle that overlaps with it, evenly distributes around this measurement axis and measures the limit axle 1～No. 4.Guarantee that 4 limit axles are consistent with the central shaft angle.Article 5, measurement axis represented moving some cloud in iterative process with the hunting zone of dead point cloud optimal registration position.Along with the increase of iteration step number, the angle of limit axle and central shaft dwindles gradually, makes the hunting zone reduce, and improves search precision.
(3) the moving point of order cloud main shaft overlaps respectively with above-mentioned 5 measurement axis.Overlap the position at each, moving some cloud hangs down precision search around 360 ° of current measurement axis rotations.In this process, with 1 ° be step-length, remembering on 360 registration position the registration error of calculating respectively with the dead point cloud altogether, and record.After finishing search, with the result of least error as this low precision search.
(4) based on the net result of low precision search, in ± 1 ° scope, carry out the high precision search with littler step-length.Moving some cloud is measured itself and the registration error of dead point cloud on each registration position, seek wherein minimum value as moving the final registration results of cloud on current measurement axis.
(5) some cloud main shafts are coincided respectively with 5 measurement axis, and on each position, carry out respectively and hang down precision search and high precision search step described in above-mentioned steps (3), (4), obtain the optimal registration position on every measurement axis, each position attaches a registration error.Choose minimum value from 5 errors, the registration position of this minimum value correspondence becomes dynamic and static some cloud optimal registration of this iteration position.
The registration error method for solving is as follows in the described step (3):
(3.1) each point in the moving point of the traversal cloud is in regular turn sought nearest corresponding point in the dead point cloud, it is right to form point.This point is observed man-to-man relation to need and is set up, and not reproducible to utilize same point in the dead point cloud to form difference right.
(3.2) calculate the right spacing of every bit, ask for somewhat right range averaging value, with this registration error as 2 clouds.
The present invention's advantage compared with prior art is:
(1) the present invention selects the unique point that the meets the same terms direction as a cloud in 2 clouds, adopts the method for iteration travel through minimum error of fitting position between 2 clouds of searching in diminishing hunting zone, realizes accurate registration.
(2) the present invention starts with from defining point cloud direction, by the optimal registration position of searching in the scope of constantly dwindling, relatively ginseng is calculated 2 clouds, thereby obtains optimum solution.It is auxiliary that this computation process need not to prepare registration Algorithm, possesses stronger fault-tolerance.
(3) the invention belongs to the interior ergodic algorithm of limited range, meeting is according to a possible registration position, the attitude of moving some cloud in the parameter setting search certain space, so it is right to need not to form point of fixity between dynamic and static 2 clouds in computation process.This characteristic need not when making the manual selected characteristic point of operating personnel to consider that point to corresponding relation, has improved work efficiency.
(4) pass through key parameter (angle α between primitive axis
0, angle economy k) modification, the present invention can be implemented in the choice between hunting zone and the search speed, thereby adapts to different registration environment.
Description of drawings
Fig. 1 is method realization flow figure of the present invention;
Fig. 2 is the original state of moving (P), quiet (X) model point cloud among the present invention;
Fig. 3 is the state after moving (P), quiet (X) model point cloud center overlap among the present invention;
Fig. 4 is the result after the main shaft match among the present invention;
Fig. 5 is the present invention's measurement axis structure in the iteration first;
Fig. 6 is the low precision search process of moving some cloud among the present invention;
Fig. 7 is moving some cloud level precision search process among the present invention;
Fig. 8 is the search procedure of moving some cloud on the different measuring axle among the present invention;
Fig. 9 is 10 the iteration results and the iteration path synoptic diagram of model point cloud among the present invention.
Embodiment
As shown in Figure 1, the present invention comprises following calculation procedure: main shaft registration, measurement axis generate, hang down precision search, high precision search and an iteration result and generate.Main shaft registration link is responsible for defining point cloud direction and is finished initial registration; Measurement axis generates link and provides the hunting zone of working as in time iteration for moving some cloud; Low precision search adopts the method for traversal to seek the optimal registration attitude of moving some cloud on current measurement axis; The high precision search is responsible for obtaining better optimal registration attitude with littler step length searching site error on the basis of low precision search; One time the iteration result generates the net result that link is responsible for generating according to each locational measurement result an iteration.Concrete steps are as follows:
Existing 2 cloud P, X subject to registration, as shown in Figure 2.P is moving some cloud, and X is the dead point cloud.The each point numbering is respectively p-1～4 and x-1～4 in 2 clouds.Here, what suppose that P point cloud and X point cloud describe is the same feature of same object, and P and X count identically, possess the registration condition.Here generate, hang down precision search, high precision search and an iteration generation as a result etc. 5 by main shaft registration, measurement axis and go on foot the registration of finishing 2 clouds.
(1) P, X main shaft registration
The most important condition of registration is that 2 cloud geometric centers overlap.Since use hypercomplex number rotary process point of rotation cloud, before calculating, need the geometric center of 2 clouds of assurance to overlap with initial point, as shown in Figure 3.
Then, define each some cloud middle distance geometric center point solstics representative point cloud direction separately, the line of this solstics and geometric center point is some cloud main shaft, and 2 cloud main shafts are fitted within together, as shown in Figure 4.P-1 point and the x-1 point in the X point cloud in this routine P point cloud are respectively to put cloud middle distance geometric center point farthest separately, have represented the direction of putting cloud separately, thus behind the main shaft registration these 2 with the true origin three point on a straight line.
So far, P, 2 cloud main shafts of X registration are finished.The main shaft registration provides simple initial registration of a step for the whole registration of 2 clouds, and in ensuing iterative computation, the major axes orientation of moving some cloud and the attitude of some cloud on this direction can constantly be revised, to seek the optimal registration position of P, 2 clouds of X.
(2) measurement axis generates
The juche idea that measurement axis generates is a scope convergent traversal search gradually, just allows some clouds travel through the error of calculating with the dead point cloud in all possible position, tries to achieve minimum value.But different with the limited quantity target in the array traversal, the possible position quantity of moving some cloud is unlimited in the space.In this case, need to cook up for moving some cloud artificially the position subject to registration of limited quantity, and order moving some cloud error of calculation on these positions, obtain optimum solution.
In each step iteration, the variform measurement axis that produces according to the iteration step number is exactly the concrete manifestation of P point cloud position subject to registration, as shown in Figure 5.
Five axles of numbering 0～4 are the measurement axis in the first step iteration.Owing to use the hypercomplex number spinning solution, so five are all risen and start from initial point.Wherein, No. 0 axle overlaps 1 with moving some cloud main shaft
No. 4 axles be distributed in fifty-fifty No. 0 axle around, with the angle of No. 0 axle be 30 °.The initial angle α of this measurement axis
0Not fixed value, can revise according to actual conditions.In all the other iterative process after the iteration first, measurement axis angle α
nNeed to reduce gradually according to a certain angle economy k ∈ (0,1), as shown in Equation (1):
α
n＝α
0×k
(n-1) (1)
The hunting zone that could guarantee moving some cloud main shaft like this dwindles along with the increase of iterations.Constant because of single iteration measurement axis quantity, so moving some cloud main shaft search precision in the hunting zone that dwindles improves, reach the purpose of high registration accuracy.
In the calculating of this step, operating personnel can determine angle α between primitive axis according to actual conditions
0And the value of angle economy k, make algorithm in different working environments, can make adaptive variation.Make that n is an iterations, S
nFor moving the scope that some cloud main shaft was searched in all the n time iteration, following formula is arranged:
When n → ∞, have:
As seen, when angle economy k value hour, S is less for the overall search scope, algorithm convergence is very fast.For example, when getting α=30 °, k=0.5, have
Be that algorithm can be finished dynamic and static some cloud main shaft deviation less than 60 ° situation.It is few that this method is applicable to that ginseng is calculated some cloud point number, and shape is the situation of rule; And for complex-shaped some cloud, main shaft step of registration described in (1) has may occur error more greatly, so need to reduce speed of convergence, expansion hunting zone.For example, when getting α=90 °, when the k value is constant, obtaining S
∞=180 °, this scope can cover the possible registration position of the moving institute of some cloud main shaft in this iteration, does not have the search dead angle.
(3) low precision search
If the possible position of moving some cloud in each iteration stipulated in the generation of five measurement axis, the purpose of coarse search is exactly to seek the optimum posture of moving some cloud on each bar measurement axis so, just under moving some cloud main shaft and state that the measurement of correlation axle overlaps with the optimal registration position of dead point cloud.Search procedure as shown in Figure 6.
Shown in Figure 6 be the process of some clouds (P) around No. 0 measurement axis wheel measuring.The measurement step-length is that 1 ° index dial is plotted on the normal plane of No. 0 measurement axis.A moving some cloud rotates a circle around No. 0 axle, measures the registration error of itself and dead point cloud on each may registration position.
By rotating 360 ° around measurement axis, P point cloud can be to obtain 360 registration accuracy measured values under 1 ° the situation measuring step-length.This optimum solution wherein is exactly the result of this low precision search.
(4) high precision search
The high precision search is the little step length searching that carries out on the basis of low precision search, and purpose is to improve the quality of low precision search.After low precision search was finished, program can obtain the aggregated error value E of each measurement point
i(0≤i≤359) in these 360 error amounts, can obtain to have minimum error values E
MinThe angle number of some position, this is the result of low precision search.In the scope of this some position ± 1 °, the moving point of order cloud is with littler step-length Step
hRotating also around current measurement axis, calculation level can obtain 2/Step to site error
hIndividual exact solution.In these exact solutions, select the error reckling, can obtain moving some cloud in the locational optimal registration result of current measurement axis, as shown in Figure 7.
In the calculating shown in Figure 7, the high precision step-size in search is 0.04 °.In the search scale shown in Fig. 7 bottom right, middle long scale mark is the final position of low precision search, and the short scale mark in both sides is the high precision hunting zone.
(5) one times the iteration result generates
In iterative computation, a moving some cloud main shaft needs respectively and 0～No. 4 measurement axis match, and finishes above-mentioned (3), the search of (4) two steps on this position, as shown in Figure 8.Among Fig. 8, moving some cloud main shaft overlaps with No. 1 measurement axis after the search mission of finishing No. 0 measurement axis, and has set up on the normal plane of No. 1 axle when time required index dial of measurement.In said process, moving some cloud needs to seek optimum registration position on each measurement axis, then seek the registration error minimum position from 5 optimum registration position, as the result of this iteration.Final result of calculation is (10 iteration) as shown in Figure 9, and pyramid structure wherein is made of 5 measurement axis summits in all previous iterative process, have represented the hunting zone of moving some cloud main shaft to change.
As mentioned above, the present invention utilizes the geometric characteristic of three-dimensional point cloud, gives a cloud direction attribute, by direction and the attitude of some cloud on this direction of constantly revising moving some cloud main shaft in iterative computation, seeks the optimal registration position of itself and dead point cloud.This algorithm has brought stronger fault-tolerance to a susceptibility of cloud shape for calculating, the some cloud that registration initial position error is bigger effectively, and need not in 2 clouds each point and form fixedly that corresponding point are right, alleviated the operation burden in the registration process.
Recited above only is the embodiment that embodies the fast three-dimensional point cloud searching method for registering that the present invention is based on a cloud shape.The present invention is not limited to the foregoing description.Instructions of the present invention is to be used to describe, and does not limit the scope of claim.For those skilled in the art, obviously a lot of replacements, improvement and variation can be arranged.All employings are equal to the technical scheme of replacement or equivalent transformation formation, all drop in the protection domain of requirement of the present invention.
Claims (2)
1. fast three-dimensional point cloud searching method for registering is characterized in that performing step is as follows:
(1) under the situation that 2 cloud geometric center point overlap, define 2 cloud middle distances geometric center point distance separately, be that initial point is a cloud direction apart from the solstics, the line of this solstics and geometric center point is a cloud main shaft, to move a cloud main shaft and fit within, finish the match of two subject to registration somes cloud main shafts with dead point cloud main shaft;
(2) be starting point with 2 cloud geometric center point, with moving some cloud main shaft is direction, generate the measuring center axle that overlaps with moving some cloud main shaft, and guarantee that the measuring center direction of principal axis is consistent with moving some cloud major axes orientation, around this measuring center axle, evenly distribute 4 and measure the limit axle, guarantee that 4 measurement limit axles are consistent with measuring center axle clamp angle; In first time iteration, 4 angles of measuring limit axle and central shaft are called the initial angle α of measurement axis
0, in all the other the iteration links except that iteration first, introduce angle economy k ∈ (0,1), make the central shaft of the n time iteration and axle clamp angle, the limit α that satisfies condition
n=α
0* k
(n-1), along with the increase of iteration step number, the angle of limit axle and central shaft dwindles gradually, makes the hunting zone reduce, and improves search precision;
(3) some cloud main shafts are overlapped respectively with above-mentioned 5 measurement axis, overlap the position at each, moving some cloud is around 360 ° of current measurement axis rotations, hanging down precision search, in this process, is step-length with 1 °, remembering the registration error of calculating respectively on 360 registration position with the dead point cloud altogether, and record, finish search after, with the result of least error as this low precision search;
(4) to hang down the net result of precision search, in ± 1 ° scope, carry out the high precision search with littler step-length, moving some cloud is measured itself and the registration error of dead point cloud on each registration position, seek wherein minimum value as moving the final registration results of cloud on current measurement axis;
(5) some cloud main shafts are coincided respectively with 5 measurement axis, and on each position, carry out respectively and hang down precision search and high precision search step described in above-mentioned steps (3), (4), obtain the optimal registration position on every measurement axis, each position attaches a registration error, choose minimum value from 5 errors, the registration position of this minimum value correspondence becomes dynamic and static some cloud optimal registration of this iteration position.
2. a kind of fast three-dimensional point cloud searching and matching method according to claim 1 is characterized in that: the registration error method for solving is as follows in the described step (3):
(3.1) each point in the moving point of the traversal cloud is in regular turn sought nearest corresponding point in the dead point cloud, it is right to form point, and this point is observed man-to-man relation to need and set up, and not reproducible to utilize same point in the dead point cloud to form difference right;
(3.2) calculate the right spacing of every bit, ask for somewhat right range averaging value, with this registration error as 2 clouds.
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Cited By (8)
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| CN103810747A (en) * | 2014-01-29 | 2014-05-21 | 辽宁师范大学 | Three-dimensional point cloud object shape similarity comparing method based on two-dimensional mainstream shape |
| CN105359163A (en) * | 2013-07-01 | 2016-02-24 | 三菱电机株式会社 | Method for fitting primitive shapes to a set of 3D points |
| CN105488535A (en) * | 2015-12-05 | 2016-04-13 | 乔付 | Three-dimensional point cloud matching method |
| CN106570905A (en) * | 2016-11-02 | 2017-04-19 | 北京控制工程研究所 | Initial attitude verification method of non-cooperative target point cloud |
| CN108052101A (en) * | 2017-12-06 | 2018-05-18 | 北京奇虎科技有限公司 | The method for relocating and device of robot |
| CN112287481A (en) * | 2020-10-27 | 2021-01-29 | 上海设序科技有限公司 | Mechanical design scheme searching method and device based on three-dimensional point cloud |
| CN112449754A (en) * | 2019-07-04 | 2021-03-05 | 深圳市大疆创新科技有限公司 | Data encoding method, data decoding method, equipment and storage medium |
| CN113345005A (en) * | 2021-06-17 | 2021-09-03 | 南京工业大学 | Finite random search method suitable for target ball center resolving |
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| CN105359163A (en) * | 2013-07-01 | 2016-02-24 | 三菱电机株式会社 | Method for fitting primitive shapes to a set of 3D points |
| CN105359163B (en) * | 2013-07-01 | 2019-08-30 | 三菱电机株式会社 | Method for cell shapes to be fitted to the set of 3D point |
| CN103810747A (en) * | 2014-01-29 | 2014-05-21 | 辽宁师范大学 | Three-dimensional point cloud object shape similarity comparing method based on two-dimensional mainstream shape |
| CN105488535B (en) * | 2015-12-05 | 2018-06-05 | 海南热带海洋学院 | A kind of three-dimensional point cloud matching process |
| CN105488535A (en) * | 2015-12-05 | 2016-04-13 | 乔付 | Three-dimensional point cloud matching method |
| CN106570905B (en) * | 2016-11-02 | 2019-05-24 | 北京控制工程研究所 | A kind of noncooperative target point cloud initial attitude verification method |
| CN106570905A (en) * | 2016-11-02 | 2017-04-19 | 北京控制工程研究所 | Initial attitude verification method of non-cooperative target point cloud |
| CN108052101A (en) * | 2017-12-06 | 2018-05-18 | 北京奇虎科技有限公司 | The method for relocating and device of robot |
| CN108052101B (en) * | 2017-12-06 | 2021-12-21 | 北京奇虎科技有限公司 | Robot repositioning method and device |
| CN112449754A (en) * | 2019-07-04 | 2021-03-05 | 深圳市大疆创新科技有限公司 | Data encoding method, data decoding method, equipment and storage medium |
| CN112449754B (en) * | 2019-07-04 | 2024-03-08 | 深圳市大疆创新科技有限公司 | Data encoding and decoding method, device and storage medium |
| CN112287481A (en) * | 2020-10-27 | 2021-01-29 | 上海设序科技有限公司 | Mechanical design scheme searching method and device based on three-dimensional point cloud |
| CN112287481B (en) * | 2020-10-27 | 2023-11-21 | 上海设序科技有限公司 | Mechanical design scheme searching method and device based on three-dimensional point cloud |
| CN113345005A (en) * | 2021-06-17 | 2021-09-03 | 南京工业大学 | Finite random search method suitable for target ball center resolving |
| CN113345005B (en) * | 2021-06-17 | 2023-09-12 | 南京工业大学 | Finite random search method suitable for target ball center calculation |
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